Senior Machine Learning Engineer

Responsibilities

• Own the design, development, maintenance, and evolution of the in-house AIOps / ML / LLM platform, including related cloud and on-premise Kubernetes solutions.
• Translate client, security, compliance, and internal requirements into practical platform designs with cross‑functional teams.
• Build and operate production ML / LLM workflows, including retraining, deployment, inference serving, monitoring, rollback, and optimisation.
• Troubleshoot production issues across application, infrastructure, networking, Linux, Kubernetes, and ML serving layers.

Qualifications / Requirements

• Strong software/platform engineering fundamentals, including system design, API design, distributed systems, scalability, reliability, observability, authentication/authorization, testing, and maintainable code design.
• Practical understanding of the ML / LLM lifecycle, including data pipelines, model training/retraining, evaluation, experiment tracking, deployment, monitoring, and production feedback loops.
• Strong development experience in Python, with working proficiency in Go and C++ for reading, debugging, maintaining, and extending existing production codebases.
• Strong Linux, networking, and Kubernetes fundamentals, including production troubleshooting, service connectivity, ingress, resource limits, workload debugging, and deployment operations.
• Experience designing, deploying, and operating production platforms on AWS, Azure, GCP, or on‑premise environments.
• Experience building CI/CD, automation, and MLOps / LLMOps workflows for production ML / LLM systems.
• Strong communication skills and ability to work with AI, deployment, infrastructure, and security teams.

Good to Have

• Deep experience operating Kubernetes in bare‑metal, air‑gapped, or restricted on‑premise environments.
• Experience with MLflow, Kubeflow, vLLM, TensorRT, TGI, or similar ML / LLM platform tools.
• Exposure to TypeScript / React or Java‑based services.